Current methods for target detection and tracking in images (e.g. radar or light images), such as constant false alarm rate (CFAR), may be ineffective in an instance in which the target has an amplitude near clutter levels or where there is a high clutter density. Even without clutter, a single target crossing a second target with a slow crossing-rate may cause challenges in tracking. The failure to associate a target in dense scenes, such as scenes with multiple targets, or scenes with targets in clutter, may prevent tracking or isolation of target detections in densely arranged targets and/or clutter.
Kalman and other conventional tracker/filters, may require well established associated measurements, which may not be possible in dense clutter or with targets with unremarkable amplitude. Euclidian distance is used by conventional tracker/filters, including Kalman, as a metric of association. However these distances go to zero at each crossing, making the Euclidian distance nearly useless in dense scenes. Feature based associations may be used as an alternative to Euclidean distance. However, feature associations have limited capability in a dense scene, where targets are not isolated from the dense clutter. Radar polarization may provide some relief, in an instance in which a co-cell does not falsify the association. Lacking association, target extent and motion may not be observable in dense scenes.
Coherent integration may be utilized to suppress clutter, but may require long interrogation time before adequate suppression allows for association and tracking by conventional tracker/filters.